Spectrum Sensing with Matrix Completion IZMA-SD for Cognitive Radio Networks
DOI:
https://doi.org/10.31908/19098367.1165Keywords:
Cognitive radio, Interest zone matrix approximation, matrix completion, nuclear norm, spectrum sensing, singular value decomposition, Standard deviation, sub-NyquistAbstract
Due to the growth of wireless networks, efficient use of the spectrum is necessary, a solution is Cognitive Radio. In one of the stages of this technology, spectrum sensing is carried out, that is, determining on a frequency whether there are primary users and, if they do not exist, occupying the available spectrum;this is achieved by applying sensing techniques, each technique requires hardware resources and can identify different characteristics of a signal. Nowadays, high propagation frequencies are used, it is necessary for the processing stage to perform a Sub Nyquist sampling, that is, less than twice the maximum frequency. An alternative solution is to use an algorithm based on Matrix Completion, called by the authors like IZMA-SD. The results show that in different signals sampled at% 75 of Nyquist and under different SNR, when the algorithm is applied, the reconstruction of the signal is performed, to which the sensing techniques can be applied.
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